Senior Machine Learning Engineer

Summary

Join Unite Us as a Senior Machine Learning Engineer and build, maintain, and optimize core infrastructure for ML model development, deployment, and operation. Collaborate with data scientists and engineers to ensure high-quality datasets and efficient data pipelines. You will streamline model lifecycles, manage model migrations to AWS SageMaker, and optimize scoring pipelines. This role requires extensive experience in machine learning, MLOps, and AWS technologies. The position is remote and offers a competitive salary and comprehensive benefits package.

Requirements

• At least 5+ years of experience as a Machine Learning Engineer, Data Scientist or Data Engineer
• At least 3+ years of experience in developing and implementing Machine Learning infrastructure and MLOps (Machine Learning Operations) within a cloud environment, specifically utilizing AWS Sagemaker and Snowflake
• Hands-on experience in building, optimizing, and maintaining data pipelines, architectures, and data sets to support machine learning initiatives
• Strong proficiency in Python, SQL and expertise in utilizing AWS automation tools for streamlining and automating processes
• Experience in implementing and utilizing monitoring and metrics systems to track and evaluate the performance of predictive models
• Experience in model monitoring and ML ops including containers and orchestration

Responsibilities

• Model Development and Deployment
• Automation and Efficiency: Streamline the entire model lifecycle, from initial development and training to deployment and retraining, using automation tools and best practices to accelerate model delivery and iteration
• Timely Model Deployment and Scoring: Empower data scientists with the ability to rapidly deploy models and generate scores for evaluation, facilitating faster experimentation and model selection
• Model Migration: Manage the migration of existing models from legacy systems to the AWS SageMaker platform, ensuring compatibility and leveraging cloud-based capabilities
• Engineering Standards: Define and enforce engineering best practices for model development, deployment, and maintenance, ensuring code quality, scalability, and maintainability
• Scoring Pipeline Optimization: Continuously optimize model scoring pipelines to eliminate errors, improve performance, and reduce latency
• Resource Adjustment: Proactively adjust instance resources based on data scientist requirements, balancing cost and performance for optimal model development and deployment
• Enable and operationalize Large Language Models (LLMs) for production use cases
• Data Collaboration and Management
• High-Quality Datasets: Collaborate with data engineers to manage and curate high-quality datasets for both internal and external use by downstream users and applications, ensuring data accuracy, completeness, and reliability
• Infrastructure for ML Solutions: Actively participate in the development and maintenance of infrastructure to support scalable machine learning solutions, including data storage, compute resources, and model deployment platforms
• Optimized Data Pipelines: Work closely with the Data Engineering team to design, implement, and maintain optimized data pipeline architectures that can efficiently handle large and complex datasets, ensuring data is processed and delivered in a timely manner
• Data-Related Technical Support: Provide stakeholders with data-related technical support and solutions, addressing their data infrastructure needs and helping them leverage data effectively
• Data Separation and Segregation: Maintain strict data separation and segregation practices in accordance with relevant data policies and regulations, ensuring data privacy, security, and compliance
• Data Preparation Tools: Collaborate with data scientists to develop and maintain data preparation tools that streamline their workflows and enable them to efficiently prepare data for analysis and modeling

Preferred Qualifications

Prior experience prompting large language models and building RAG applications is a plus

Benefits

• Medical, Dental, and Vision
• We offer insurance to team members and eligible partners and dependents, including unlimited virtual mental health and acute medical visits
• Mental health benefits, such as the Employee Assistance Program (EAP) and wellness platform subscription, are available to all team members
• Flexible Time Off
• Take what you need, including volunteer days and mental health days. We also offer 14 paid, company-wide holidays
• Paid Parental Leave
• Adoptive parents are included
• Employee Resource Groups
• Choose to join any of our ERGs, which celebrate and support a diverse and inclusive workplace
• Spending Accounts
• We offer tax-advantaged health savings accounts (HSAs), flexible spending accounts (FSAs), and commuter benefits
• 401(k) + Employer Match
• Enjoy matching, immediate vesting and financial wellness resources
• Life and AD&D - a company paid benefit, with the option to purchase additional coverage for yourself and your dependents
• Disability Coverage
• Accident Insurance
• Pet Insurance
• As part of this work at home job, we will provide you with all the necessary equipment to perform your duties, including a computer, mouse, keyboard as well as other items on our approved list of WFH supplies